Method and apparatus for automated image analysis of biological specimens

ABSTRACT

A method including acquiring images of medical slides at a plurality of different focus positions, determining a position which produces a maximum value of pixel values relative to a pixel value mean, wherein said determining comprises using a pixel value mean as a coarse estimate of coarse focus position, and subsequently refining said coarse focus position to find a fine focus position, and wherein said refining comprises fitting to a polynomial, and using a specified portion of the polynomial as a fine estimate of focus position, and producing a focus control signal that is related to said maximum value to control a focus position.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 11/685,717,filed Mar. 13, 2007, which is a continuation of application Ser. No.10/746,515, filed Dec. 24, 2003, now U.S. Pat. No. 7,190,818 issued Mar.13, 2007, which is a continuation of application Ser. No. 09/495,461,filed Feb. 1, 2000, now U.S. Pat. No. 6,718,053 issued Apr. 6, 2003,which is a continuation-in-part of application Ser. No. 09/344,308,filed Jun. 24, 1999, now U.S. Pat. No. 6,418,236 issued Jul. 9, 2002,which claims the benefit of U.S. Provisional Application No. 60/129,384,filed Apr. 13, 1999, and is a continuation-in-part of application Ser.No. 08/827,268, filed Mar. 28, 1997, now U.S. Pat. No. 6,151,405 issuedNov. 21, 2000, which is a continuation-in-part of application Ser. No.08/758,436, filed on Nov. 27, 1996, now U.S. Pat. No. 6,215,892 issuedApr. 10, 2001, which claims the benefit of U.S. Provisional ApplicationNo. 60/026,805, filed Nov. 30, 1995, each of which are hereby fullyincorporated herein by reference.

FIELD

The disclosure relates generally to light microscopy and, moreparticularly, to automated light microscopic methods and an apparatusfor detection of objects in a sample.

BACKGROUND

In the field of medical diagnostics and research including oncology, thedetection, identification, quantification and characterization of cellsof interest, such as cancer cells, through testing of biologicalspecimens is an important aspect of diagnosis and research. Typically, abiological specimen such as bone marrow, lymph nodes, peripheral blood,cerebrospinal fluid, urine, effusions, fine needle aspirates, peripheralblood scrapings or other materials are prepared by staining the specimento identify cells of interest. One method of cell specimen preparationis to react a specimen with a specific probe which can be a monoclonalantibody, a polyclonal antiserum, or a nucleic acid which is reactivewith a component of the cells of interest, such as tumor cells. Thereaction may be detected using an enzymatic reaction, such as alkalinephosphatase or glucose oxidase or peroxidase to convert a solublecolorless substrate to a colored insoluble precipitate, or by directlyconjugating a dye or a fluorescent molecule to the probe. Examination ofbiological specimens in the past has been performed manually by either alab technician or a pathologist. In the manual method, a slide preparedwith a biological specimen is viewed at a low magnification under amicroscope to visually locate candidate cells or objects of interest.Those areas of the slide where cells of interest are located are thenviewed at a higher magnification to confirm the objects or cells, suchas tumor or cancer cells. The manual method is time consuming and proneto error including missing areas of the slide. Automated cell analysissystems have been developed to improve the speed and accuracy of thetesting process. One known interactive system includes a single highpower microscope objective for scanning a rack of slides, portions ofwhich have been previously identified for assay by an operator. In thatsystem, the operator first scans each slide at a low magnificationsimilar to the manual method and notes the points of interest on theslide for later analysis. The operator then stores the address of thenoted location and the associated function in a data file.

Once the points of interest have been located and stored by theoperator, the slide is then positioned in an automated analysisapparatus which acquires images of the slide at the marked points andperforms an image analysis.

BRIEF SUMMARY

A problem with the foregoing automated system is the continued need foroperator input to initially locate cell objects for analysis. Suchcontinued dependence on manual input can lead to errors including cellsor objects of interest being missed. Such errors can be criticalespecially in assays for so-called rare events, e.g., finding one tumorcell in a cell population of one million normal cells. Additionally,manual methods can be extremely time consuming and ‘can require a highdegree of training to identify and/or quantify cells. This is not onlytrue for tumor cell identification and detection, but also for otherapplications ranging from neutrophil alkaline phosphatase assays,reticulocyte counting and maturation assessment, and others. Theassociated manual labor leads to a high cost for these procedures inaddition to the potential errors that can arise from long, tediousmanual examinations. A need exists, therefore, for an improved automatedcell analysis system which can quickly and accurately scan large amountsof biological material on a slide. Accordingly, the disclosure providesa method and apparatus for automated cell analysis which eliminates theneed for operator input to locate cell objects for analysis.

In accordance with the disclosure, a slide prepared with a biologicalspecimen and reagent is placed in a slide carrier which preferably holdsfour slides. The slide carriers are loaded into an input hopper of theautomated system. The operator may then enter data identifying the size,shape and location of a scan area on each slide, or, preferably, thesystem automatically locates a scan area for each slide during slideprocessing. An operator then activates the system for slide processing.Alternatively, the processing parameters of the slide may be identifiedby a bar code present on the slide or slide carrier. At systemactivation, a slide carrier is positioned on an X-Y stage, the entireslide is rapidly scanned at a low magnification, typically 10×. At eachlocation of the scan, a low magnification image is acquired andprocessed to detect candidate objects of interest. Preferably, color,size and shape are used to identify objects of interest. The location ofeach candidate object of interest is stored.

At the completion of the low level scan for each slide in the carrier onthe stage, the optical system is adjusted to a high magnification suchas 40× or 60×, and the X-Y stage is positioned to the stored locationsfor the candidate objects of interest on each slide in the carrier.

A high magnification image is acquired for each candidate object ofinterest and a series of image processing steps are performed to confirmthe analysis which was performed at low magnification. A highmagnification image is stored for each confirmed object of interest.

These images are then available for retrieval by a pathologist, orcytotechnologist to review for final diagnostic evaluation. Havingstored the location of each object of interest, a mosaic comprised ofthe candidate objects of interest for a slide may be generated andstored. The pathologist or cytotechnologist may view the mosaic or mayalso directly view the slide at the location of an object of interest inthe mosaic for further evaluation. The mosaic may be stored on magneticmedia for future reference or may be transmitted to a remote site forreview and/or storage. The entire process involved in examining a singleslide takes on the order of 2-15 minutes depending on scan area size andthe number of detected candidate objects of interest. The disclosure hasutility in the field of oncology for the early detection of minimalresidual disease (“micrometastases”). Other useful applications includeprenatal diagnosis of fetal cells in maternal blood and in the field ofinfectious diseases to identify pathogens and viral loads, alkalinephosphatase assessments, reticulocyte counting, and others.

The processing of images acquired in the automated scanning of thedisclosure preferably includes the steps of transforming the image to adifferent color space; filtering the transformed image with a low passfilter; dynamically thresholding the pixels of the filtered image tosuppress background material; performing a morphological function toremove artifacts from the thresholded image; analyzing the thresholdedimage to determine the presence of one or more regions of connectedpixels having the same or similar color; and categorizing every regionhaving a size greater than a minimum size as a candidate object ofinterest.

According to another aspect of the disclosure, the scan area isautomatically determined by scanning the slide; acquiring an image ateach slide position; analyzing texture information of each image todetect the edges of the specimen; and storing the locationscorresponding to the detected edges to define the scan area. Accordingto yet another aspect of the disclosure, automated focusing of theoptical system is achieved by initially determining a focal plane froman array of points or locations in the scan area. The derived focalplane enables subsequent rapid automatic focusing in the low powerscanning operation. The focal plane is determined by determining properfocal positions across an array of locations and performing an analysissuch as a least squares fit of the array of focal positions to yield afocal plane across the array. Preferably, a focal position at eachlocation is determined by incrementing the position of a Z stage for afixed number of coarse and fine iterations. At each iteration, an imageis acquired and a pixel variance or other optical parameter about apixel mean for the acquired image is calculated to form a set ofvariance data. A least squares fit is performed on the variance dataaccording to a known function. The peak value of the least squares fitcurve is selected as an estimate of the best focal position.

In another aspect of the disclosure, another focal position method forhigh magnification locates a region of interest centered about acandidate object of interest within a slide which was located during ananalysis of the low magnification images. The region of interest ispreferably n columns wide, where n is a power of 2. The pixels of thisregion are then processed using a Fast Fourier Transform to generate aspectra of component frequencies and corresponding complex magnitude foreach frequency component. Magnitude of the frequency components whichrange from 25% to 75% of the maximum frequency component are squared andsummed to obtain the total power for the region of interest. Thisprocess is repeated for other Z positions and the Z positioncorresponding to the maximum total power for the region of interest isselected as the best focal position. This process is preferably used toselect a Z position for regions of interest for slides containingpreferably, the complex neutrophils stained with Fast Red to identifyalkaline phosphatase in cell cytoplasm and counterstained withhematoxylin to identify the nucleus of the neutrophil cell. This focalmethod may be used with other stains and types of biological specimens,as well.

According to still another aspect of the disclosure, a method andapparatus for automated slide handling is provided. A slide is mountedonto a slide carrier with a number of other slides side-by-side. Theslide carrier is positioned in an input feeder with other slide carriersto facilitate automatic analysis of a batch of slides. The slide carrieris loaded onto the X-Y stage of the optical system for the analysis ofthe slides thereon.

Subsequently, the first slide carrier is unloaded into an output feederafter automatic image analysis and the next carrier is automaticallyloaded.

Also provided is an apparatus for processing slides according to themethods above. The apparatus includes a computer having at least onesystem processor with image processing capability, a computer monitor,an input device, a power supply and a microscope subsystem. Themicroscope subsystem includes an optical sensing array for acquiringimages. A two dimensional motion stage for sample movement and for focusadjustment and input and output mechanisms for multiple sample analysisand storage.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of embodiments of the invention includingvarious novel details of construction and combinations of parts will nowbe more particularly described with reference to the accompanyingdrawings and pointed out in the claims. It will be understood that theparticular apparatus of embodiments of the invention is shown by way ofillustration only and not as a limitation. The principles and featurescan be employed in varied and numerous embodiments without departingfrom the scope of the invention.

FIG. 1 is a perspective view of an apparatus for automated cell analysisof an embodiment of the present invention.

FIG. 2 is a block diagram of the apparatus shown in FIG. 1.

FIG. 3 is a block diagram of the system processor of FIG. 2.

FIG. 4 is a plan view of the apparatus of FIG. 1 having the housingremoved.

FIG. 5 is a side view of a microscope subsystem of the apparatus of FIG.1

FIG. 6 a is a top view of a slide carrier for use with the apparatus ofFIG. 1.

FIG. 6 b is a bottom view of the slide carrier of FIG. 6 a.

FIG. 7 a is a top view of an automated slide handling subsystem of theapparatus of FIG. 1

FIG. 7 b is a partial cross-sectional view of the automated slidehandling subsystem of FIG. 7 a taken on line A-A.

FIG. 8 is an end view of the input module of the automated slidehandling subsystem;

FIGS. 8 a-8 d illustrate the input operation of the automatic slidehandling subsystem;

FIGS. 9 a-9 d illustrate the output operation of the automated slidehandling subsystem.

FIG. 10 is a flow diagram of the procedure for automatically determininga scan area.

FIG. 11 shows the scan path on a prepared slide in the procedure of FIG.10.

FIG. 12 illustrates an image of a field acquired in the procedure ofFIG. 10.

FIG. 13A is a flow diagram of a preferred procedure for determining afocal position.

FIG. 13B is a flow diagram of a preferred procedure for determining afocal position for neutrophils stained with Fast Red and counterstainedwith hematoxylin.

FIG. 14 is a flow diagram of a procedure for automatically determininginitial focus

FIG. 15 shows an array of slide positions for use in the procedure ofFIG. 14.

FIG. 16 is a flow diagram of a procedure for automatic focusing at ahigh magnification.

FIG. 17A is a flow diagram of an overview of the preferred process tolocate and identify objects of interest in a stained biological specimenon a slide.

FIG. 17B is a flow diagram of a procedure for color space conversion.

FIG. 18 is a flow diagram of a procedure for background suppression viadynamic thresholding.

FIG. 19 is a flow diagram of a procedure for morphological processing.

FIG. 20 is a flow diagram of a procedure for blob analysis.

FIG. 21 is a flow diagram of a procedure for image processing at a highmagnification.

FIG. 22 illustrates a mosaic of cell images produced by the apparatus.

FIG. 23 is a flow diagram of a procedure for estimating the number ofnucleated cells in a field.

FIGS. 24 a and 24 b illustrates the apparatus functions available in auser interface of the, apparatus.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring now to the figures, an apparatus for automated cell analysisof biological specimens is generally indicated by reference numeral 10as shown in perspective view in FIG. 1 and in block diagram form in FIG.2. The apparatus 10 comprises a microscope subsystem 32 housed in ahousing 12. The housing 12 includes a slide carrier input hopper 16 anda slide carrier output hopper 18. A door 14 in the housing 12 securesthe microscope subsystem from the external environment. A computersubsystem comprises a computer 22 having at least one system processor23, and a communications modem 29. The computer subsystem furtherincludes a computer monitor 26 and other external peripherals includingstorage device 21, a pointing device, such as a track ball device 30, auser input device, such as a touch screen, keyboard, or voicerecognition unit 28 and color printer 35. An external power supply 24 isalso shown for power outage protection. The apparatus 10 furtherincludes an optical sensing array 42, such as a camera, preferably a CCDcamera, for acquiring images. Microscope movements are under the controlof system processor 23 through a number of microscope subsystemfunctions described further in detail. An automatic slide feed mechanismin conjunction with X-Y stage 38 provide automatic slide handling in theapparatus 10. An illumination light source 48 projects light onto theX-Y stage 38 which is subsequently imaged through the microscopesubsystem 32 and acquired through optical sensing array 42 forprocessing by the system processor 23. A Z stage or focus stage 46 undercontrol of the system processor 23 provides displacement of themicroscope subsystem in the Z plane for focusing. The microscopesubsystem 32 further includes a motorized objective turret 44 forselection of objectives.

The purpose of the apparatus 10 is for the unattended automatic scanningof prepared microscope slides for the detection and counting ofcandidate objects of interest such as normal and abnormal cells, e.g.,tumor cells. The preferred embodiment may be utilized for rare eventdetection in which there may be only one candidate object of interestper several hundred thousand normal cells, e.g., one to five candidateobjects of interest per 2 square centimeter area of the slide. Theapparatus 10 automatically locates and counts candidate objects ofinterest and estimates normal cells present in a biological specimen onthe basis of color, size and shape characteristics. A number of stainsare used to preferentially stain candidate objects of interest andNormal cells different colors so that such cells can be distinguishedfrom each other.

As noted in the background section above, a biological specimen may beprepared with a reagent to obtain a colored insoluble precipitate. Theapparatus of the disclosure is used to detect this precipitate as acandidate object of interest. During operation of the apparatus 10, apathologist or laboratory technician mounts prepared slides onto slidecarriers. A slide carrier 60 is illustrated in FIG. 8 and will bedescribed further below. Each slide carrier holds up to 4 slides. Up to25 slide carriers are then loaded into input hopper 16. The operator canspecify the size, shape and location of the area to be scanned oralternatively, the system can automatically locate this area. Theoperator then commands the system to begin automated scanning of theslides through a graphical user interface. Unattended scanning beginswith the automatic loading of the first carrier and slide onto theprecision motorized X-Y stage 38. A bar code label affixed to the slideor slide carrier is read by a bar code reader 33 during this loadingoperation.

Each slide is then scanned at a user selected low microscopemagnification, for example, 10×, to identify candidate cells based ontheir color, size and shape characteristics. The locations coordinate oraddress of candidate objects of interest are stored, such as by X-Y,r-.θ, polar, vector or other coordinate systems well known in the art,until scanning is completed.

After the low magnification scanning is completed, the apparatusautomatically returns to each candidate cell, reimages and refocuses ata higher magnification such as 40× and performs further analysis toconfirm the cell candidate. Alternatively, the system may process lowmagnification images by reconstructing the image from individual fieldsof view and then determining objects of interest. In this manner,objects of interest that overlap more than one objective field of viewmay be identified. The apparatus stores an image of the object ofinterest for later review by a pathologist. All results and images canbe stored to a storage device 21 such as a removable hard drive, DATtape, local hard drive, optical disk, or transmitted to a remote sitefor review or storage. The stored images for each slide can be viewed ina mosaic of images for further review.

Having described the overall operation of the apparatus 10 from a highlevel, the further details of the apparatus will now be described.Referring to FIG. 3, the microscope subsystem control is shown in moredetail. The microscope control includes a number of subsystems. Theapparatus system processor 23 controls these subsystems. The systemprocessor 23 controls a set of motor-control subsystems 114 through 124which control the input and output feeder, the motorized turret 44, theX-Y stage 38, and the Z stage 46 (FIG. 2). The system processor 23further controls an illumination controller 106 for control of substageillumination 48. The light output from the halogen light bulb whichsupplies illumination for the system can vary over time due to bulbaging, changes in optical alignment, and other factors. In addition,slides which have been “over stained” can reduce the camera exposure toan unacceptable level. In order to compensate for these effects, theillumination controller 106 is included. This controller is used inconjunction with camera and image collection adjustments to compensatefor the variations in light level. The light control software samplesthe output from the camera at intervals (such as between loading ofslide carriers), and commands the controller to adjust the light orimage collection functions to the desired levels. In this way, lightcontrol is automatic and transparent to the user and adds no additionaltime to system operation. The system processor 23 is preferably a highperformance processor of at least 200 MHz, for example the systemprocessor may comprise dual parallel Intel Pentium 200 MHZ devices.

Referring now to FIGS. 4 and 5, further detail of the apparatus 10 isshown. FIG. 4 shows a plan view of the apparatus 10 with the housing 12removed. A portion of the automatic slide feed mechanism 37 is shown tothe left of the microscope subsystem 32 and includes slide carrierunloading assembly 34 and unloading platform 36 which in conjunctionwith slide carrier output hopper 18 function to receive slide carrierswhich have been analyzed. Vibration isolation mounts 40, shown infurther detail in FIG. 5, are provided to isolate the microscopesubsystem 32 from mechanical shock and vibration that can occur in atypical laboratory environment. In addition to external sources ofvibration, the high speed operation of the X-Y stage 38 can inducevibration into the microscope subsystem 32. Such sources of vibrationcan be isolated from the electrooptical subsystems to avoid anyundesirable effects on image quality. The isolation mounts 40 comprise aspring 40 a and piston 40 b submerged in a high viscosity silicon gelwhich is enclosed in an elastomer membrane bonded to a casing to achievedamping factors on the order of 17 to 20%

The automatic slide handling feature of the disclosure will now bedescribed. The automated slide handling subsystem operates on a singleslide carrier at a time. A slide carrier 60 is shown in FIGS. 6 a and 6b which provide a top view and a bottom view respectively. The slidecarrier 60 includes up to four slides 70. The carrier 60 includes ears64 for hanging the carrier in the output hopper 18. An undercut 66 andpitch rack 68 are formed at the top edge of the slide carrier 60 formechanical handling of the slide carrier. A keyway cutout 65 is formedin one side of the carrier 60 to facilitate carrier alignment. Aprepared slide 72 mounted on the slide carrier 60 includes a sample area72 a and a bar code label area 72 b.

FIG. 7 a provides a top view of the slide handling subsystem whichcomprises a slide input module 15, a slide output module 17 and X-Ystage drive belt 50. FIG. 7 b provides a partial cross-sectional viewtaken along line A-A of FIG. 7 a. The slide input module 15 comprises aslide carrier input hopper 16, loading platform 52 and slide carrierloading subassembly 54. The input hopper 16 receives a series of slidecarriers 60 (FIGS. 6 a and 6 b) in a stack on loading platform 52. Aguide key 57 protrudes from a side of the input hopper 16 to which thekeyway cutout 65 (FIG. 6 a) of the carrier is fit to achieve properalignment. The input module 15 further includes a revolving indexing cam56 and a switch 90 mounted in the loading platform 52, the operation ofwhich is described further below. The carrier loading subassembly 54comprises an infeed drive belt 59 driven by a motor 86. The infeed drivebelt 59 includes a pusher tab 58 for pushing the slide carrierhorizontally toward the X-Y stage 38 when the belt is driven. A homingswitch 95 senses the pusher tab 58 during a revolution of the belt 59.Referring specifically to FIG. 7 a, the X-Y stage 38 is shown with xposition and y position motors 96 and 97 respectively which arecontrolled by the system processor 23 (FIG. 3) and are not consideredpart of the slide handling subsystem. The X-Y stage 38 further includesan aperture 55 for allowing illumination to reach the slide carrier. Aswitch 91 is mounted adjacent the aperture 55 for sensing contact withthe carrier and thereupon activating a motor 87 to drive stage drivebelt 50 (FIG. 7 b). The drive belt 50 is a double sided timing belthaving teeth for engaging pitch rack 68 of the carrier 60 (FIG. 6 b)

The slide output module 17 includes slide carrier output hopper 18,unloading platform 36 and slide carrier unloading subassembly 34. Theunloading subassembly 34 comprises a motor 89 for rotating the unloadingplatform 36 about shaft 98 during an unloading operation describedfurther below. An outfeed gear 93 driven by motor 88 rotatably engagesthe pitch rack 68 of the carrier 60 (FIG. 6 b) to transport the carrierto a rest position against switch 92. A spring loaded hold-downmechanism holds the carrier in place on the unloading platform 36.

The slide handling operation will now be described.

Referring to FIG. 8, a series of slide carriers 60 are shown stacked ininput hopper 16 with the top edges 60 a aligned. As the slide handlingoperation begins, the indexing cam 56 driven by motor 85 advances onerevolution to allow only one slide carrier to drop to the bottom of thehopper 16 and onto the loading platform 52.

FIGS. 8 a-8 d show the cam action in more detail. The cam 56 includes ahub 56 a to which are mounted upper and lower leaves 56 b and 56 crespectively. The leaves 56 b and 56 c are semicircular projectionsoppositely positioned and spaced apart vertically. In a first positionshown in FIG. 8 a, the upper leaf 56 b supports the bottom carrier atthe undercut portion 66. At a position of the cam 56 rotated 180°, shownin FIG. 8 b, the upper leaf 56 b no longer supports the carrier andinstead the carrier has dropped slightly and is supported by the lowerleaf 56 c. FIG. 8 c shows the position of the cam 56 rotated 2700wherein the upper leaf 56 b has rotated sufficiently to begin to engagethe undercut 66 of the next slide carrier while the opposite facinglower leaf 56 c still supports the bottom carrier. After a full rotationof 360° as shown in FIG. 8 d, the lower leaf 56 c has rotated oppositethe carrier stack and no longer supports the bottom carrier which nowrests on the loading platform 52. At the same position, the upper leaf56 b supports the next carrier for repeating the cycle.

Referring again to FIGS. 7 a and 7 b, when the carrier drops to theloading platform 52, the contact closes switch 90 which activates motors86 and 87. Motor 86 drives the infeed drive belt 59 until the pusher tab58 makes contact with the carrier and pushes the carrier onto the X-Ystage drive belt 50. The stage drive belt 50 advances the carrier untilcontact is made with switch 91, the closing of which begins the slidescanning process described further herein.

Upon completion of the scanning process, the X-Y stage 38 moves to anunload position and motors 87 and 88 are activated to transport thecarrier to the unloading platform 36 using stage drive belt 50. Asnoted, motor 88 drives outfeed gear 93 to engage the carrier pitch rack68 of the carrier 60 (FIG. 6 b) until switch 92 is contacted.

Closing switch 92 activates motor 89 to rotate the unloading platform36.

The unloading operation is shown in more detail in end views of theoutput module 17 (FIGS. 9 a-9 d). In FIG. 9 a, the unloading platform 36is shown in a horizontal position supporting a slide carrier 60. Thehold-down mechanism 94 secures the carrier 60 at one end. FIG. 9 b showsthe output module 17 after motor 89 has rotated the unloading platform36 to a vertical position, at which point the spring loaded hold-downmechanism 94 releases the slide carrier 60 into the output hopper 18.The carrier 60 is supported in the output hopper 18 by means of ears 64(FIGS. 6 a and 6 b). FIG. 9 c shows the unloading platform 36 beingrotated back towards the 20 horizontal position. As the platform 36rotates upward, it contacts the deposited carrier 60 and the upwardmovement pushes the carrier toward the front of the output hopper 18.FIG. 9 d shows the unloading platform 36 at its original horizontalposition after having output a series of slide carriers 60 to the outputhopper 18.

Having described the overall system and the automated slide handlingfeature, the aspects of the apparatus 10 relating to scanning, focusingand image processing will now be described in further detail.

In some cases, an operator will know ahead of time where the scan areaof interest is on the slide. Conventional preparation of slides forexamination provides repeatable and known placement of the sample on theslide. The operator can therefore instruct the system to always scan thesame area at the same location of every slide which is prepared in thisfashion. But there are other times in which the area of interest is notknown, for example, where slides are prepared manually with a knownsmear technique. One feature of embodiments of the inventionautomatically determines the scan area using a texture or densityanalysis process. FIG. 10 is a flow diagram that describes theprocessing associated with the automatic location of a scan area. Asshown in this figure, the basic method is to pre-scan the entire slidearea to determine texture features that indicate the presence of a smearand to discriminate these areas from dirt and other artifacts.

At each location of this raster scan, an image such as in FIG. 12 isacquired and analyzed for texture information at steps 204 and 206.Since it is desired to locate the edges of the smear sample within agiven image, texture analyses are conducted over areas called windows78, which are smaller than the entire image as shown in FIG. 12. Theprocess iterates the scan across the slide at steps 208, 210, 212 and214.

In the interest of speed, the texture analysis process is performed at alower magnification, preferably at a 4× objective. One reason to operateat low magnification is to image the largest slide area at any one time.Since cells do not yet need to be resolved at this stage of the overallimage analysis, the 4× magnification is preferred.

On a typical slide, as shown in FIG. 11, a portion 72 b of the end ofthe slide 72 is reserved for labeling with identification information.Excepting this label area, the entire slide is scanned in a raster scanfashion to yield a number of adjacent images. Texture values for eachwindow include the pixel variance over a window, the difference betweenthe largest and smallest pixel value within a window, and otherindicators. The presence of a smear raises the texture values comparedwith a blank area.

One problem with a smear from the standpoint of determining its locationis its non-uniform thickness and texture. For example, the smear islikely to be relatively thin at the edges and thicker towards the middledue to the nature of the smearing process. To accommodate thisnonuniformity, texture analysis provides a texture value for eachanalyzed area. The texture value tends to gradually rise as the scanproceeds across a smear from a thin area to a thick area, reaches apeak, and then falls off again to a lower value as a thin area at theedge is reached. The problem is then to decide from the series oftexture values the beginning and ending, or the edges, of the smear. Thetexture values are fit to a square wave waveform since the texture datadoes not have sharp beginnings and endings.

After conducting this scanning and texture evaluation operation, onemust determine which areas of elevated texture values represent thedesired smear 74, and which represent undesired artifacts. This isaccomplished by fitting a step function, on a line by line basis, to thetexture values in step 216. This function, which resembles a singlesquare wave beginning at one edge and ending at the other edge andhaving an amplitude, provides the means for discrimination. Theamplitude of the best-fit step function is utilized to determine whethersmear or dirt is present since relatively high values indicate smear. Ifit is decided that smear is present, the beginning and endingcoordinates of this pattern are noted until all lines have beenprocessed, and the smear sample area defined at 218.

After an initial focusing operation described further herein, the scanarea of interest is scanned to acquire images for image analysis. Thepreferred method of operation is to initially perform a complete scan ofthe slide at low magnification to identify and locate candidate objectsof interest, followed by further image analysis of the candidate objectsof interest at high magnification in order to confirm the objects ascells. An alternate method of operation is to perform high magnificationimage analysis of each candidate object of interest immediately afterthe object has been identified at low magnification. The lowmagnification scanning then resumes, searching for additional candidateobjects of interest. Since it takes on the order of a few seconds tochange objectives, this alternate method of operation would take longerto complete.

To identify structure in tissue that cannot be captured in a singlefield of view image or a single staining technique, the disclosureprovides a method for histological reconstruction to analyze many fieldsof view on potentially many slides simultaneously. The method couplescomposite images in an automated manner for processing and analysis. Aslide on which is mounted a cellular specimen stained to identifyobjects of interest is supported on a motorized stage. An image of thecellular specimen is generated, digitized, and stored in memory. As theviewing field of the objective lens is smaller than the entire cellularspecimen, a histological reconstruction is made. These stored images ofthe entire tissue section may then be placed together in an order suchthat the H/E stained slide is paired with the immunohistochemistry slideso that analysis of the images may be performed simultaneously.

The operator can pre-select a magnification level to be used for thescanning operation. A low magnification such as the 10× objective ispreferred for the scanning operation since a larger area can beinitially analyzed for each acquired scan image. The overall detectionprocess for a cell includes a combination of decisions made at both low(10×) and high magnification (40×) levels. Decision making at the 10×magnification level is broader in scope, i.e., objects that loosely fitthe relevant color, size and shape characteristics are identified at the10× level.

Analysis at the 40× magnification level then proceeds to refine thedecision making and confirm objects as likely cells or candidate objectsof interest. For example, at the 40× level it is not uncommon to findthat some objects that were identified at 10× are artifacts which theanalysis process will then reject. In addition, closely packed objectsof interest appearing at 10× are separated at the 40× level

In a situation where a cell straddles or overlaps adjacent image fields,image analysis of the individual adjacent image fields could result inthe cell being rejected or undetected. To avoid missing such cells, thescanning operation compensates by overlapping adjacent image fields inboth the x and y directions. An overlap amount greater than half thediameter of an average cell is preferred. In the preferred embodiment,the overlap is specified as a percentage of the image field in the x andy directions. Alternatively, a reconstruction method as described abovemay be used to reconstruct the image from multiple fields of view. Thereconstructed image is then analyzed and processed to find objects ofinterest.

The time to complete an image analysis can vary depending upon the sizeof the scan area and the number of candidate cells, or objects ofinterest identified. For one example, in the preferred embodiment, acomplete image analysis of a scan area of two square centimeters inwhich 50 objects of interest are confirmed can be performed in about 12to 15 minutes. This example includes not only focusing, scanning andimage analysis but also the saving of 40× images as a mosaic on harddrive 21 (FIG. 2). Consider the utility of the disclosure in a “rareevent” application where there may be one, two or a very small number ofcells of interest located somewhere on the slide. To illustrate thenature of the problem by analogy, if one were to scale a slide to thesize of a football field, a tumor cell, for example, would be about thesize of a bottle cap. The problem is then to rapidly search the footballfield and find the very small number of bottle caps and have a highcertainty that none have been missed.

However the scan area is defined, an initial focusing operation must beperformed on each slide prior to scanning. This is required since slidesdiffer, in general, in their placement in a carrier. These differencesinclude slight (but significant) variations of tilt of the slide in itscarrier. Since each slide must remain in focus during scanning, thedegree of tilt of each slide must be determined. This is accomplishedwith an initial focusing operation that determines the exact degree oftilt, so that focus can be maintained automatically during scanning.

The initial focusing operation and other focusing operations to bedescribed later utilize a focusing method based on processing of imagesacquired by the system. This method was chosen for its simplicity overother methods including use of IR beams reflected from the slide surfaceand use of mechanical gauges. These other methods also would notfunction properly when the specimen is protected with a coverslip. Thepreferred method results in lower system cost and improved reliabilitysince no additional parts need be included to perform focusing. FIG. 13Aprovides a flow diagram describing the “focus point” procedure. Thebasic method relies on the fact that the pixel value variance (orstandard deviation) taken about the pixel value mean is maximum at bestfocus. A “brute-force” method could simply step through focus, using thecomputer controlled Z, or focus stage, calculate the pixel variance ateach step, and return to the focus position providing the maximumvariance. Such a method would be too time consuming. Therefore,additional features were added as shown in FIG. 13A.

These features include the determination of pixel variance at arelatively coarse number of focal positions, and then the fitting of acurve to the data to provide a faster means of determining optimalfocus. This basic process is applied in two steps, coarse and fine.

During the coarse step at 220-230, the Z stage is stepped over auser-specified range of focus positions, with step sizes that are alsouser-specified. It has been found that for coarse focusing, these dataare a close fit to a Gaussian function. Therefore, this initial set ofvariance versus focus position data are least-squares fit to a Gaussianfunction at 228. The location of the peak of this Gaussian curvedetermines the initial or coarse estimate of focus position for input tostep 232.

Following this, a second stepping operation 232-242 is performedutilizing smaller steps over a smaller focus range centered on thecoarse focus position. Experience indicates that data taken over thissmaller range are generally best fit by a second order polynomial. Oncethis least squares fit is performed at 240, the peak of the second ordercurve provides the fine focus position at 244.

FIG. 14 illustrates a procedure for how this focusing method is utilizedto determine the orientation of a slide in its carrier. As shown, focuspositions are determined, as described above, for a 3×3 grid of pointscentered on the scan area at 264. Should one or more of these points lieoutside the scan area, the method senses this at 266 by virtue of lowvalues of pixel variance. In this case, additional points are selectedcloser to the center of the scan area. FIG. 15 shows the initial arrayof points 80 and new point 82 selected closer to the center. Once thisarray of focus positions is determined at 268, a least squares plane isfit to this data at 270. Focus points lying too far above or below thisbest-fit plane are discarded at 272 (such as can occur from a dirtycover glass over the scan area), and the data is then refit. This planeat 274 then provides the desired Z position information for maintainingfocus during scanning.

After determination of the best-fit focus plane, the scan area isscanned in an X raster scan over the scan area as described earlier.During scanning, the X stage is positioned to the starting point of thescan area, the focus (Z) stage is positioned to the best fit focusplane, an image is acquired and processed as described later, and thisprocess is repeated for all points over the scan area. In this way,focus is maintained automatically without the need for time-consumingrefocusing at points during scanning.

Prior to confirmation of cell objects at a 40× or 60× level, arefocusing operation is conducted since the use of this highermagnification requires more precise focus than the best-fit planeprovides. FIG. 16 provides the flow diagram for this process. As may beseen, this process is similar to the fine focus method described earlierin that the object is to maximize the image pixel variance. This isaccomplished by stepping through a range of focus positions with the Zstage at 276, 278, calculating the image variance at each position at278, fitting a second order polynomial to these data at 282, andcalculating the peak of this curve to yield an estimate of the bestfocus position at 284, 286. This final focusing step differs fromprevious ones in that the focus range and focus step sizes are smallersince this magnification requires focus settings to within 0.5 micron orbetter. It should be noted that for some combinations of cell stainingcharacteristics, improved focus can be obtained by numerically selectingthe focus position that provides the largest variance, as opposed toselecting the peak of the polynomial. In such cases, the polynomial isused to provide an estimate of best focus, and a final step selects theactual Z position giving highest pixel variance. It should also be notedthat if at any time during the focusing process at 40× or 60× theparameters indicate that the focus position is inadequate, the systemautomatically reverts to a coarse focusing process as described abovewith reference to FIG. 13A. This ensures that variations in specimenthickness can be accommodated in an expeditious manner. For somebiological specimens and stains, the focusing methods discussed above donot provide optimal focused results. For example, certain white bloodcells known as neutrophils may be stained with Fast Red, a commonlyknown stain, to identify alkaline phosphatase in the cytoplasm of thecells. To further identify these cells and the material within them, thespecimen may be counterstained with hematoxylin to identify the nucleusof the cells. In cells so treated, the cytoplasm bearing alkalinephosphatase becomes a shade of red proportionate to the amount ofalkaline phosphatase in the cytoplasm and the nucleus becomes blue.However, where the cytoplasm and nucleus overlap, the cell appearspurple. These color combinations appear to preclude the finding of afocused Z position using the focus processes discussed above.

In an effort to find a best focal position at high magnification, afocus method, such as the one shown in FIG. 13B, may be used. Thatmethod begins by selecting a pixel near the center of a candidate objectof interest (Block 248) and defining a region of interest centered aboutthe selected pixel (Block 250). Preferably, the width of the region ofinterest is a number of columns which is a power of 2. This widthpreference arises from subsequent processing of the region of interestpreferably using a one dimensional Fast Fourier Transform (FFT)technique. As is well known within the art, processing columns of pixelvalues using the FFT technique is facilitated by making the number ofcolumns to be processed a power of two. While the height of the regionof interest is also a power of two in the preferred embodiment, it neednot be unless a two dimensional FFT technique is used to process theregion of interest. After the region of interest is selected, thecolumns of pixel values are processed using the preferred onedimensional FFT to determine a spectra of frequency components for theregion of interest (Block 252). The frequency spectra ranges from DC tosome highest frequency component. For each frequency component, acomplex magnitude is computed. Preferably, the complex magnitudes forthe frequency components which range from approximately 25% of thehighest component to approximately 75% of the highest component aresquared and summed to determine the total power for the region ofinterest (Block 254). Alternatively, the region of interest may beprocessed with a smoothing window, such as a Hanning window, to reducethe spurious high frequency components generated by the FFT processingof the pixel values in the region of interest. Such preprocessing of theregion of interest permits all complex magnitude over the completefrequency range to be squared and summed. After the power for a regionhas been computed and stored (Block 256), a new focal position isselected, focus adjusted (Blocks 258, 260), and the process repeated.

After each focal position has been evaluated, the one having thegreatest power factor is selected as the one best in focus (Block 262).

The following describes the image processing methods which are utilizedto decide whether a candidate object of interest such as a stained tumorcell is present in a given image, or field, during the scanning process.

Candidate objects of interest which are detected during scanning arereimaged at higher (40× or 60×) magnification, the decision confirmed,and a region of interest for this cell saved for later review. The imageprocessing includes color space conversion, low pass filtering,background suppression, artifact suppression, morphological processing,and blob analysis. One or more of these steps can optionally beeliminated. The operator is provided with an option to configure thesystem to perform any or all of these steps and whether to performcertain steps more than once or several times in a row. It should alsobe noted that the sequence of steps may be varied and thereby optimizedfor specific reagents or reagent combinations; however, the sequencedescribed herein is preferred. It should be noted that the imageprocessing steps of low pass filtering, thresholding, morphologicalprocessing, and blob analysis are generally known image processingbuilding blocks.

An overview of the preferred process is shown in FIG. 17A. The preferredprocess for identifying and locating candidate objects of interest in astained biological specimen on a slide begins with an acquisition ofimages obtained by scanning the slide at low magnification (Block 288).Each image is then converted from a first color space to a second colorspace (Block 290) and the color converted image is low pass filtered(Block 292). The pixels of the low pass filtered image are then comparedto a threshold (Block 294) and, preferably, those pixels having a valueequal to or greater than the threshold are identified as candidateobject of interest pixels and those less than the threshold aredetermined to be artifact or background pixels. The candidate object ofinterest pixels are then morphologically processed to identify groups ofcandidate object of interest pixels as candidate objects of interest(Block 296). These candidate objects of interest are then compared toblob analysis parameters (Block 298) to further differentiate candidateobjects of interest from objects which do not conform to the blobanalysis parameters and, thus, do not warrant further processing. Thelocation of the candidate objects of interest may be stored prior toconfirmation at high magnification. The process continues by determiningwhether the candidate objects of interest have been confirmed (Block300). If they have not been confirmed, the optical system is set to highmagnification (Block 302) and images of the slide at the locationscorresponding to the candidate objects of interest identified in the lowmagnification images are acquired (Block 288). These images are thencolor converted (Block 290), low pass filtered (Block 292), compared toa threshold (Block 294), morphologically processed (Block 296), andcompared to blob analysis parameters (Block 298) to confirm whichcandidate objects of interest located from the low magnification imagesare objects of interest. The coordinates of the objects of interest arethen stored for future reference (Block 303)

In general, the candidate objects of interest, such as tumor cells, aredetected based on a combination of characteristics, including size,shape, and color. The chain of decision making based on thesecharacteristics preferably begins with a color space conversion process.The optical sensing array coupled to the microscope subsystem outputs acolor image comprising a matrix of 640×480 pixels. Each pixel comprisesred, green and blue (RGB) signal values.

It is desirable to transform the matrix of RGB values to a differentcolor space because the difference between candidate objects of interestand their background, such as tumor and normal cells, may be determinedfrom their respective colors. Specimens are generally stained with oneor more industry standard stains (e.g., DAB, New Fuchsin, AEC) which are“reddish” in color. Candidate objects of interest retain more of thestain and thus appear red while normal cells remain unstained. Thespecimens may also be counterstained with hematoxylin so the nuclei ofnormal cells or cells not containing an object of interest appear blue.In addition to these objects, dirt and debris can appear as black, gray,or can also be lightly stained red or blue depending on the stainingprocedures utilized. The residual plasma or other fluids also present ona smear may also possess some color.

In the color conversion operation, a ratio of two of the RGB signalvalues is formed to provide a means for discriminating colorinformation. With three signal values for each pixel, nine differentratios can be formed: R/R, R/G, R/B, G/G, G/B, G/R, B/B, BIG, B/R. Theoptimal ratio to select depends upon the range of color informationexpected in the slide specimen. As noted above, typical stains used fordetecting candidate objects of interest such as tumor cells arepredominantly red, as opposed to predominantly green or blue. Thus, thepixels of a cell of interest which has been stained contain a redcomponent which is larger than either the green or blue components. Aratio of red divided by blue (R/B) provides a value which is greaterthan one for tumor cells but is approximately one for any clear or whiteareas on the slide. Since the remaining cells, i.e., normal cells,typically are stained blue, the R/B ratio for pixels of these lattercells yields values of less than one. The R/B ratio is preferred forclearly separating the color information typical in these applications.

FIG. 17B illustrates the flow diagram by which this conversion isperformed. In the interest of processing speed, the conversion isimplemented with a look up table. The use of a look up table for colorconversion accomplishes three functions: 1) performing a divisionoperation; 2) scaling the result for processing as an image having pixelvalues ranging from 0 to 255; and 3) defining objects which have lowpixel values in each color band (R,G,B) as “black” to avoid infiniteratios (i.e., dividing by zero). These “black” objects are typicallystaining artifacts or can be edges of bubbles caused by pasting acoverglass over the specimen.

Once the look up table is built at 304 for the specific color ratio(i.e., choices of tumor and nucleated cell stains), each pixel in theoriginal RGB image is converted at 308 to produce the output. Since itis of interest to separate the red stained tumor cells from blue stainednormal ones, the ratio of color values is then scaled by a userspecified factor. As an example, for a factor of 128 and the ratio of(red pixel value)/(blue pixel value), clear areas on the slide wouldhave a ratio of 1 scaled by 128 for a final X value of 128. Pixels whichlie in red stained tumor cells would have X value greater than 128,while blue stained nuclei of normal cells would have value less than128. In this way, the desired objects of interest can be numericallydiscriminated. The resulting 640×480 pixel matrix, referred to as theX-image, is a gray scale image having values ranging from 0 to 255.

Other methods exist for discriminating color information. One classicalmethod converts the RGB color information into another color space, suchas HSI (hue, saturation, intensity) space. In such a space, distinctlydifferent hues such as red, blue, green, yellow, may be readilyseparated. In addition, relatively lightly stained objects may bedistinguished from more intensely stained ones by virtue of differingsaturations. However, converting from RGB space to HSI space requiresmore complex computation. Conversion to a color ratio is faster; forexample, a full image can be converted by the ratio technique of thedisclosure in about 30 ms while an HSI conversion can take severalseconds.

In yet another approach, one could obtain color information by taking asingle color channel from the optical sensing array. As an example,consider a blue channel, in which objects that are red are relativelydark. Objects which are blue, or white, are relatively light in the bluechannel. In principle, one could take a single color channel, and simplyset a threshold wherein everything darker than some threshold iscategorized as a candidate object of interest, for example, a tumorcell, because it is red and hence dark in the channel being reviewed.However, one problem with the single channel approach occurs whereillumination is not uniform. Nonuniformity of illumination results innon-uniformity across the pixel values in any color channel, forexample, tending to peak in the middle of the image and dropping off atthe edges where the illumination falls off. Performing thresholding onthis non-uniform color information runs into problems, as the edgessometimes fall below the threshold, and therefore it becomes moredifficult to pick the appropriate threshold level. However, with theratio technique, if the values of the red channel fall off from centerto edge, then the values of the blue channel also fall off center toedge, resulting in a uniform ratio at non-uniform lighting. Thus, theratio technique is more immune to illumination.

As described, the color conversion scheme is relatively insensitive tochanges in color balance, i.e., the relative outputs of the red, green,and blue channels. However, some control is necessary to avoid camerasaturation, or inadequate exposures in any one of the color bands. Thiscolor balancing is performed automatically by utilizing a calibrationslide consisting of a clear area, and a “dark” area having a knownoptical transmission or density. The system obtains images from theclear and “dark” areas, calculates “white” and “black” adjustments forthe image-frame grabber or digitizer processor 25, and thereby providescorrect color balance.

In addition to the color balance control, certain mechanical alignmentsare automated in this process. The center point in the field of view forthe various microscope objectives as measured on the slide can vary byseveral (or several tens of) microns. This is the result of slightvariations in position of the microscope objectives 44 a as determinedby the turret 44 (FIG. 4), small variations in alignment of theobjectives with respect to the system optical axis, and other factors.Since it is desired that each microscope objective be centered at thesame point, these mechanical offsets must be measured and automaticallycompensated.

This is accomplished by imaging a test slide which contains arecognizable feature or mark. An image of this pattern is obtained bythe system with a given objective, and the position of the markdetermined. The system then rotates the turret to the next lensobjective, obtains an image of the test object, and its position isredetermined. Apparent changes in position of the test mark are recordedfor this objective. This process is continued for all objectives.

Once these spatial offsets have been determined, they are automaticallycompensated for by moving the stage 38 by an equal (but opposite) amountof offset during changes in objective. In this way, as different lensobjectives are selected, there is no apparent shift in center point orarea viewed. A low pass filtering process precedes thresholding. Anobjective of thresholding is to obtain a pixel image matrix having onlycandidate objects of interest, such as tumor cells above a thresholdlevel and everything else below it. However, an actual acquired imagewill contain noise. The noise can take several forms, including whitenoise and artifacts. The microscope slide can have small fragments ofdebris that pick up color in the staining process and these are known asartifacts. These artifacts are generally small and scattered areas, onthe order of a few pixels, which are above the threshold. The purpose oflow pass filtering is to essentially blur or smear the entire colorconverted image. The low pass filtering process will smear artifactsmore than larger objects of interest, such as tumor cells and therebyeliminate or reduce the number of artifacts that pass the thresholdingprocess. The result is a cleaner thresholded image downstream. In thelow pass filter process, a 3×3 matrix of coefficients is applied to eachpixel in the 640×480 x-image. A preferred coefficient matrix is asfollows:

$\begin{matrix}{{1/9}\mspace{14mu}} & {1/9} & {\mspace{14mu}{1/9}} \\{{1/9}\mspace{14mu}} & {1/9} & {\mspace{14mu}{1/9}} \\{{1/9}\mspace{14mu}} & {1/9} & {\mspace{14mu}{1/9}}\end{matrix}$

At each pixel location, a 3×3 matrix comprising the pixel of interestand its neighbors is multiplied by the coefficient matrix and summed toyield a single value for the pixel of interest. The output of thisspatial convolution process is again a 640×480 matrix. As an example,consider a case where the center pixel and only the center pixel, has avalue of 255 and each of its other neighbors, top left, top, top rightand so forth, have values of 0.

This singular white pixel case corresponds to a small object. The resultof the matrix multiplication and addition using the coefficient matrixis a value of 1/9 (255) or 28 for the center pixel, a value which isbelow the nominal threshold of 128. Now consider another case in whichall the pixels have a value of 255 corresponding to a large object.Performing the low pass filtering operation on a 3×3 matrix for thiscase yields a value of 255 for the center pixel. Thus, large objectsretain their values while small objects are reduced in amplitude oreliminated. In the preferred method of operation, the low pass filteringprocess is performed on the X image twice in succession.

In order to separate objects of interest, such as a tumor cell in the ximage from other objects and background, a thresholding operation isperformed designed to set pixels within cells of interest to a value of255, and all other areas to 0. Thresholding ideally yields an image inwhich cells of interest are white and the remainder of the image isblack. A problem one faces in thresholding is where to set the thresholdlevel. One cannot simply assume that cells of interest are indicated byany pixel value above the nominal threshold of 128. A typical imagingsystem may use an incandescent halogen light bulb as a light source. Asthe bulb ages, the relative amounts of red and blue output can change.The tendency as the bulb ages is for the blue to drop off more than thered and the green. To accommodate for this light source variation overtime, a dynamic thresholding process is used whereby the threshold isadjusted dynamically for each acquired image. Thus, for each 640×480image, a single threshold value is derived specific to that image.

As shown in FIG. 18, the basic method is to calculate, for each field,the mean X value, and the standard deviation about this mean at 312. Thethreshold is then set at 314 to the mean plus an amount defined by theproduct of a (user specified) factor and the standard deviation of thecolor converted pixel values. The standard deviation correlates to thestructure and number of objects in the image. Preferably, the userspecified factor is in the range of approximately 1.5 to 2.5. The factoris selected to be in the lower end of the range for slides in which thestain has primarily remained within cell boundaries and the factor isselected to be in the upper end of the range for slides in which thestain is pervasively present throughout the slide. In this way, as areasare encountered on the slide with greater or lower backgroundintensities, the threshold may be raised or lowered to help reducebackground objects. With this method, the threshold changes in step withthe aging of the light source such that the effects of the aging arecanceled out. The image matrix resulting at 316 from the thresholdingstep is a binary image of black (0) and white (255) pixels.

As is often the case with thresholding operations such as that describedabove, some undesired areas will lie above the threshold value due tonoise, small stained cell fragments, and other artifacts. It is desiredand possible to eliminate these artifacts by virtue of their small sizecompared with legitimate cells of interest. Morphological processes areutilized to perform this function.

Morphological processing is similar to the low pass filter convolutionprocess described earlier except that it is applied to a binary image.Similar to spatial convolution, the morphological process traverses aninput image matrix, pixel by pixel, and places the processed pixels inan output matrix. Rather than calculating a weighted sum of theneighboring pixels as in the low pass convolution process, themorphological process uses set theory operations to combine neighboringpixels in a nonlinear fashion.

Erosion is a process whereby a single pixel layer is taken away from theedge of an object. Dilation is the opposite process which adds a singlepixel layer to the edges of an object. The power of morphologicalprocessing is that it provides for further discrimination to eliminatesmall objects that have survived the thresholding process and yet arenot likely tumor cells. The erosion and dilation processes that make upa morphological “open” operation preferably make small objects disappearyet allow large objects to remain. Morphological processing of binaryimages is described in detail in “Digital Image Processing”, pages127-137, G. A. Baxes, John Wiley & Sons, (1994)

FIG. 19 illustrates the flow diagram for this process.

A single morphological open consists of a single morphological erosion320 followed by a single morphological dilation 322. Multiple “opens”consist of multiple erosions followed by multiple dilations. In thepreferred embodiment, one or two morphological opens are found to besuitable.

At this point in the processing chain, the processed image containsthresholded objects of interest, such as tumor cells (if any werepresent in the original image), and possibly some residual artifactsthat were too large to be eliminated by the processes above.

FIG. 20 provides a flow diagram illustrating a blob analysis performedto determine the number, size, and location of objects in thethresholded image. A blob is defined as a region of connected pixelshaving the same “color”, in this case, a value of 255. Processing isperformed over the entire image to determine the number of such regionsat 324 and to determine the area and coordinates for each detected blobat 326. Comparison of the size of each blob to a known minimum area at328 for a tumor cell allows a refinement in decisions about whichobjects are objects of interest, such as tumor cells, and which areartifacts. The term “coordinate” or “address” is used to mean aparticular location on a slide or sample. The coordinate or address canbe identified by any number of means including, for example, X-Ycoordinates, r-Θ coordinates, and others recognized by those skilled inthe art. The location of objects identified as cells of interest in thisstage are saved for the final 40× reimaging step described below.Objects not passing the size test are disregarded as artifacts.

The processing chain described above identifies objects at the scanningmagnification as cells of interest candidates. As illustrated in FIG.21, at the completion of scanning, the system switches to the 40×magnification objective at 330, and each candidate is reimaged toconfirm the identification 332. Each 40× image is reprocessed at 334using the same steps as described above but with test parameterssuitably modified for the higher magnification (e.g. area). At 336, aregion of interest centered on each confirmed cell is saved to the harddrive for review by the pathologist.

As noted earlier, a mosaic of saved images is made available for viewingby the pathologist. As shown in FIG. 22, a series of images of cellswhich have been confirmed by the image analysis is presented in themosaic 150. The pathologist can then visually inspect the images to makea determination whether to accept (152) or reject (153) each cell image.Such a determination can be noted and saved with the mosaic of imagesfor generating a printed report.

In addition to saving the image of the cell and its region, the cellcoordinates are saved should the pathologist wish to directly view thecell through the oculars or on the image monitor. In this case, thepathologist reloads the slide carrier, selects the slide and cell forreview from a mosaic of cell images, and the system automaticallypositions the cell under the microscope for viewing.

It has been found that normal cells whose nuclei have been stained withhematoxylin are often quite numerous, numbering in the thousands per 10×image. Since these cells are so numerous, and since they tend to clump,counting each individual nucleated cell would add an excessiveprocessing burden, at the expense of speed, and would not necessarilyprovide an accurate count due to clumping. The apparatus performs anestimation process in which the total area of each field that is stainedhematoxylin blue is measured and this area is divided by the averagesize of a nucleated cell. FIG. 23 outlines this process. In thisprocess, a single color band (the red channel provides the best contrastfor blue stained nucleated cells) is processed by calculating theaverage pixel value for each field at 342, establishing two thresholdvalues (high and low) as indicated at 344, 346, and counting the numberof pixels between these two values at 348. In the absence of dirt, orother opaque debris, this provides a count of the number ofpredominantly blue pixels. By dividing this value by the average areafor a nucleated cell at 350, and looping over all fields at 352, anapproximate call count is obtained. Preliminary testing of this processindicates an accuracy with +/−15%. It should be noted that for someslide preparation techniques, the size of nucleated cells can besignificantly larger than the typical size. The operator can select theappropriate nucleated cell size to compensate for these characteristics.

As with any imaging system, there is some loss of modulation transfer(i.e. contrast) due to the modulation transfer function (MTF)characteristics of the imaging optics, camera, electronics, and othercomponents. Since it is desired to save “high quality” images of cellsof interest both for pathologist review and for archival purposes, it isdesired to compensate for these MTF losses.

An MTF compensation, or MTFC, is performed as a digital process appliedto the acquired digital images. A digital filter is utilized to restorethe high spatial frequency content of the images upon storage, whilemaintaining low noise levels. With this MTFC technology, image qualityis enhanced, or restored, through the use of digital processing methodsas opposed to conventional oil-immersion or other hardware basedmethods. MTFC is described further in “The Image Processing Handbook,”pages 225 and 337, J. C. Rues, CRC Press (1995)

Referring to FIGS. 24 a and 24 b, the functions available in a userinterface of the apparatus 10 are shown. From the user interface, whichis presented graphically on computer monitor 26, an operator can selectamong apparatus functions which include acquisition 402, analysts 404,and system configuration 406. At the acquisition level 402, the operatorcan select between manual 408 and automatic 410 modes of operation. Inthe manual mode, the operator is presented with manual operations 409.Patient information 414 regarding an assay can be entered at 412. In theanalysis level 404, review 416 and report 418 functions are madeavailable. At the review level 416, the operator can select a montagefunction 420. At this montage level, a pathologist can performdiagnostic review functions including visiting an image 422,accept/reject of cells 424, nucleated cell counting 426, accept/rejectof cell counts 428, and saving of pages at 430. The report level 418allows an operator to generate patient reports 432. In the configurationlevel 406, the operator can select to configure preferences at 434,input operator information 437 at 436, create a system log at 438, andtoggle a menu panel at 440. The configuration preferences include scanarea selection functions at 442, 452; montage specifications at 444, barcode handling at 446, default cell counting at 448, stain selection at450, and scan objective selection at 454.

1. A method of focusing, comprising: acquiring, with an imaging device,a first electronic image of at least a portion of a slide presenting amedical sample; varying a focus of said slide by changing a distancebetween said slide and the imaging device to acquire a second electronicimage; based on the acquiring and varying, fitting at least onecharacteristic of the first and second images to a Gaussian function;using said fitting to select an optimum focus by positioning the slideat a specified distance from the imaging device relative to a peak ofsaid Gaussian function as an estimate of a coarse focus position;determining a fine focus position by finding a least squares fit to asecond order polynomial; and positioning the imaging device at the finefocus position.
 2. A method of focusing comprising; acquiring images ofa medical slide at a plurality of focus positions using an imagingdevice; using a computer to determine a focus position for the imagingdevice, the computer programmed with an algorithm for first determininga position that produces a maximum value of pixel values relative to apixel value mean, said determining comprising using said pixel valuemean as a coarse estimate of coarse focus position, and next refiningsaid coarse focus position to find a fine focus position, said refiningcomprising fitting to a polynomial and using a specified portion of thepolynomial as a fine estimate of focus position; producing a focuscontrol signal that is related to said maximum value to position theimaging device at said focus position.
 3. A method for histologicalreconstruction to simultaneously analyze multiple fields of view, saidmethod comprising: providing a stained cellular specimen foridentification of objects of interest; generating images of the cellularspecimen using an optical sensing array, each of the images beingsmaller than the entire stained cellular specimen; and using a processorprogrammed with an algorithm to digitize each of said images to formcorresponding digitized images, couple said stored digitized images ofthe cellular specimen together in an order to form a composite image;and pair the composite image of the stained slide with animmunohistochemistry slide so that analysis of the digitized images canbe performed simultaneously, said analysis including processing thecomposite image to find objects of interest.